Topic
Task analysis
About: Task analysis is a research topic. Over the lifetime, 10432 publications have been published within this topic receiving 283481 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: This article explored participants' developing understandings of TBLT and its suitability for New Zealand's secondary schools and found that beginning teachers were generally positive about TBLTs and perceived several challenges to its successful implementation.
Abstract: Task-based language teaching (TBLT) is an innovative learner-centred and experiential approach to modern foreign language (MFL) teaching and learning that is not without controversy in the secondary MFL classroom. This article considers one secondary-level initial teacher education programme in New Zealand in which, following school curriculum reform, TBLT has become a specific focus. Drawing on aspects of participants' critical reflections as part of the year-long programme, the article explores participants' developing understandings of TBLT and its suitability for New Zealand's secondary schools. It was found that beginning teachers were generally positive about TBLT. They also perceived several challenges to its successful implementation. Reflections after experiences in schools revealed that local school contexts, including the levels of understanding of, and receptivity to, innovation among more experienced colleagues, made a difference to successful task utilisation. The article draws some conclusi...
72 citations
TL;DR: It is shown that toddlers succeed at verbal false-belief tasks that do not require them to answer direct questions about agents' false beliefs, which reinforces claims of robust continuity in early false-Belief understanding as assessed by spontaneous-response tasks and provides researchers with new experimental tasks for exploring earlyfalse-believe understanding in neurotypical and autistic populations.
Abstract: Recent research indicates that toddlers and infants succeed at various non-verbal spontaneous-response false-belief tasks; here we asked whether toddlers would also succeed at verbal spontaneous-response false-belief tasks that imposed significant linguistic demands. We tested 2.5-year-olds using two novel tasks: a preferential-looking task in which children listened to a false-belief story while looking at a picture book (with matching and non-matching pictures), and a violation-of-expectation task in which children watched an adult 'Subject' answer (correctly or incorrectly) a standard false-belief question. Positive results were obtained with both tasks, despite their linguistic demands. These results (1) support the distinction between spontaneous- and elicited-response tasks by showing that toddlers succeed at verbal false-belief tasks that do not require them to answer direct questions about agents' false beliefs, (2) reinforce claims of robust continuity in early false-belief understanding as assessed by spontaneous-response tasks, and (3) provide researchers with new experimental tasks for exploring early false-belief understanding in neurotypical and autistic populations.
72 citations
TL;DR: The method of systematically generating visual sensing strategies based on knowledge of the assembly task to be performed, using the task analysis based on face contact relations between objects, is described.
Abstract: This paper describes a method of systematically generating visual sensing strategies based on knowledge of the assembly task to be performed. Since visual sensing is usually performed with limited resources, visual sensing strategies should be planned so that only necessary information is obtained efficiently. The generation of the appropriate visual sensing strategy entails knowing what information to extract, where to get it, and how to get it. This is facilitated by the knowledge of the task, which describes what objects are involved in the operation, and how they are assembled. In the proposed method, using the task analysis based on face contact relations between objects, necessary information for the current operation is first extracted. Then, visual features to be observed are determined using the knowledge of the sensor, which describes the relationship between a visual feature and information to be obtained. Finally, feasible visual sensing strategies are evaluated based on the predicted success probability, and the best strategy is selected. Our method has been implemented using a laser range finder as the sensor. Experimental results show the feasibility of the method, and point out the importance of task-oriented evaluation of visual sensing strategies.
72 citations
Patent•
IBM1
TL;DR: In this paper, the authors propose a method for task execution improvement, which includes: generating a baseline model for executing a task, recording a user executing the task, and comparing the baseline model to the user's execution of the task.
Abstract: A method for task execution improvement, the method includes: generating a baseline model for executing a task; recording a user executing a task; comparing the baseline model to the user's execution of the task; and providing feedback to the user based on the differences in the user's execution and the baseline model.
72 citations
TL;DR: A task-oriented user selection incentive mechanism (TRIM) is proposed, in an effort toward a task-centered design framework in MCS, which achieves feasible and efficient user selection while ensuring the privacy and security of the sensing user in M CS.
Abstract: The designs of existing incentive mechanisms in mobile crowdsensing (MCS) are primarily platform-centered or user-centered, while overlooking the multidimensional consideration of sensing task requirements. Therefore, the user selection fails to effectively address the task requirements or the relevant maximization and diversification problems. To tackle these issues, in this paper, with the aid of edge computing, we propose a task-oriented user selection incentive mechanism (TRIM), in an effort toward a task-centered design framework in MCS. Initially, an edge node is deployed to publish the sensing task according to its requirements, and constructs a task vector from multiple dimensions to maximize the satisfaction of the task requirements. Meanwhile, a sensing user constructs a user vector to formalize the personalized preferences for participating in the task response. Furthermore, by introducing a privacy-preserving cosine similarity computing protocol, the similarity level between the task vector and the user vector can be calculated, and subsequently a target user candidate set can be obtained according to the similarity level. In addition, considering the constraint of the task budget, the edge node performs a secondary sensing user selection based on the ratio of the similarity level and the expected reward of the sensing user. By designing a secure multi-party sorting protocol, enhanced by fuzzy closeness and the fuzzy comprehensive evaluation method, the target user set is determined aiming at maximizing the similarity of the task requirements and the user's preferences, while minimizing the payment of the edge node, and ensuring the fairness of the sensing user being selected. The simulation results show that TRIM achieves feasible and efficient user selection while ensuring the privacy and security of the sensing user in MCS. Among the dynamic changes of task requirements, TRIM excels with user selections reaching nearly 90% on the data quality level compliance rate and 70% on the task budget consumption ratio, superior to the other incentive mechanisms.
72 citations